Prevalence of SARS-Cov-2 antibodies and living conditions: the French national random population-based EPICOV cohort.

BACKGROUND: We aimed to estimate the seroprevalence of SARS-CoV-2 infection in France and to identify the populations most exposed during the first epidemic wave. METHODS: Random selection of individuals aged 15 years or over, from the national tax register (96% coverage). Socio-economic data, migration history, and living conditions were collected via self-computer-assisted-web or computer-assisted-telephone interviews. Home self-sampling was performed for a random subsample, to detect IgG antibodies against spike protein (Euroimmun), and neutralizing antibodies with in-house assays, in dried blood spots (DBS). RESULTS: The questionnaire was completed by 134,391 participants from May 2nd to June 2st, 2020, including 17,441 eligible for DBS 12,114 of whom were tested. ELISA-S seroprevalence was 4.5% [95% CI 3.9-5.0] overall, reaching up to 10% in the two most affected areas. High-density residences, larger household size, having reported a suspected COVID-19 case in the household, working in healthcare, being of intermediate age and non-daily tobacco smoking were independently associated with seropositivity, whereas living with children or adolescents did not remain associated after adjustment for household size. Adjustment for both residential density and household size accounted for much of the higher seroprevalence in immigrants born outside Europe, twice that in French natives in univariate analysis. CONCLUSION: The EPICOV cohort is one of the largest national representative population-based seroprevalence surveys for COVID-19. It shows the major role of contextual living conditions in the initial spread of COVID-19 in France, during which the availability of masks and virological tests was limited.

Statistical consideration of the strategy for demonstrating clinical evidence of effectiveness--one larger vs two smaller pivotal studies.

As a regulatory strategy, it is nowadays not uncommon to conduct one confirmatory pivotal clinical trial, instead of two, to demonstrate efficacy and safety in drug development. This paper is intended to investigate the statistical foundation of such an approach. The one-study approach is compared with the conventional two-study approach in terms of power, type-I error, and fundamental statistical assumptions. Necessary requirements for a single-study model is provided in order to maintain equivalent evidence as that from a two-study model. In general, one-study model is valid only under a 'one population' assumption. In addition, higher data quality and more convincing and robust results need to be demonstrated in such cases. However, when 'one-population' assumption is valid and appropriate methods are selected, a one-study model can have a better power using the same sample size. The paper also investigates statistical assumptions and methods for making an overall inference when a two-study model has been used. The methods for integrated analysis are evaluated. It is important for statisticians to select correct pooling strategy based on the project objective and statistical hypothesis.

The use of putative placebo in active control trials: two applications in a regulatory setting.

For life-threatening diseases, ethical considerations preclude the inclusion of an untreated control group in the investigation of a new therapeutic agent when a standard therapy exists. In these cases, active controlled studies are conducted, and may be planned to demonstrate either superiority or equivalence/non-inferiority of the new drug over the standard therapy (active control). In the non-inferiority study, an important aspect is the ability to detect an inferior drug (assay sensitivity). It has been suggested that assay sensitivity for a non-inferiority study should be deduced from historical data, specifically placebo controlled studies with the standard therapy. The assessment of assay sensitivity may also be important in a superiority trial that fails to demonstrate a statistically significant difference between treatments, and the sponsor attempts to determine whether there is lack of inferiority as an alternative hypothesis for regulatory approval. This paper describes two methods of putative placebo analysis for assessing assay sensitivity in active controlled trials. One approach imputes a point estimate for the odds ratio (95 per cent confidence interval) for a new drug (T) compared to a placebo control (P). A Bayesian approach calculates the posterior probability that T is superior to P, or, that T is at least k per cent as good as the active control (A) and A is more effective than P. These methods are applied in two clinical/regulatory settings: a phase III trial comparing docetaxel (Taxotere) to doxorubicin in metastatic breast cancer patients, and a phase III programme with two trials comparing enoxaparin (Lovenox) plus aspirin to unfractionated heparin plus aspirin in patients with unstable angina or non-Q-wave myocardial infarction. The methodologies presented in this paper were used in securing regulatory approval for docetaxel in the treatment of locally advanced or metastatic breast cancer after failure of prior chemotherapy, and for enoxaparin in the treatment of acute coronary syndrome.